2021
DOI: 10.1016/j.bspc.2021.102948
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Classification of sEMG signals of hand gestures based on energy features

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Cited by 27 publications
(9 citation statements)
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“…However, the average recognition accuracy of subject 7 based on the LogR algorithm reached 85%, which was significantly higher than that of subject 3 (74.6%). Although the subject's hand muscle composition is consistent, the development of the musculature is different, which will have a greater impact on the sEMG signal and gesture recognition [42][43][44][45][46][47][48][49][50][51][52][53][54]. In addition, for gesture activity recognition, the classification accuracies of HC and HO increased We acknowledge some limitations of this study.…”
Section: Discussionmentioning
confidence: 91%
See 1 more Smart Citation
“…However, the average recognition accuracy of subject 7 based on the LogR algorithm reached 85%, which was significantly higher than that of subject 3 (74.6%). Although the subject's hand muscle composition is consistent, the development of the musculature is different, which will have a greater impact on the sEMG signal and gesture recognition [42][43][44][45][46][47][48][49][50][51][52][53][54]. In addition, for gesture activity recognition, the classification accuracies of HC and HO increased We acknowledge some limitations of this study.…”
Section: Discussionmentioning
confidence: 91%
“…Zhang et al proposed an ensemble learning method based on random forests to adaptively learn the gesture features of high-density sEMG, and the results outperformed other advanced algorithms [50]. Karnam et al proposed energy features for sEMG classification, and finally the KNN classifier achieved the highest validation accuracy of 88.8%, exceeding the state-ofthe-art accuracy of 13% [51]. However, the crossindividual gesture classification performance of the above algorithms (KNN, CART, NBM, SVM, RF) lags behind the LogR algorithm (table 4), which is beyond expectation.…”
Section: Discussionmentioning
confidence: 99%
“…In [29], a classification task composed of 15 hand movements from persons with intact limbs and 12 from amputees was performed leveraging the Linear Discriminant Analysis (LDA) and SVM based on the autoregressive features. In [30], energy-based features were used with the nonparametric kNN for the classification of gestures using the sEMG recordings. In all these statistical learning methods, a key limitation is that the feature spaces could never be more expressive than the optimal spanning of the manually selected features.…”
Section: Statistical Learning Methodsmentioning
confidence: 99%
“…K. H. Lee et al demonstrated impressive accuracies of 94.0%, 87.6%, and 53.9% using ANN (Artificial Neural Networks), SVM, and logistic regression kernel classifiers for specific finger gestures [11]. Additionally, Karnam et al showcased Fine KNN's (K-nearest Neighbors) performance on the Ninapro DB1 dataset, achieving accuracies of 88.8% and 87.6% [12]. P. Kaczmarek et al investigated hand gestures using SVM and KNN, achieving high accuracies ranging from 87% to 90% [13].…”
Section: Introductionmentioning
confidence: 99%